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Spark for Data Science

You're reading from   Spark for Data Science Analyze your data and delve deep into the world of machine learning with the latest Spark version, 2.0

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Product type Paperback
Published in Sep 2016
Publisher Packt
ISBN-13 9781785885655
Length 344 pages
Edition 1st Edition
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Authors (2):
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Bikramaditya Singhal Bikramaditya Singhal
Author Profile Icon Bikramaditya Singhal
Bikramaditya Singhal
Srinivas Duvvuri Srinivas Duvvuri
Author Profile Icon Srinivas Duvvuri
Srinivas Duvvuri
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Toc

Table of Contents (12) Chapters Close

Preface 1. Big Data and Data Science – An Introduction FREE CHAPTER 2. The Spark Programming Model 3. Introduction to DataFrames 4. Unified Data Access 5. Data Analysis on Spark 6. Machine Learning 7. Extending Spark with SparkR 8. Analyzing Unstructured Data 9. Visualizing Big Data 10. Putting It All Together 11. Building Data Science Applications

Data analytics life cycle

For most real-world projects, there is some defined sequence of steps to be followed. However, there are no universally agreed upon definitions or boundaries for data analytics and data science. Generally, the term "data analytics" encompasses the techniques and processes involved in examining data, discovering useful insights, and communicating them. The term "data science" can be best treated as an interdisciplinary field drawing from statistics, computer science, and mathematics. Both terms deal with processing raw data to derive knowledge or insights, usually in an iterative fashion, and some people use them interchangeably.

Based on diverse business requirements, there are different ways of approaching problems but there is no unique standard process that fits in well with all possible scenarios. A typical process workflow can be summarized as a cycle of formulating a question, exploring, hypothesizing, validating the hypothesis, analyzing...

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